Publications by authors named "Fabian Theis"

256 Publications

Integrated intra- and intercellular signaling knowledge for multicellular omics analysis.

Mol Syst Biol 2021 03;17(3):e9923

Faculty of Medicine and Heidelberg University Hospital, Institute of Computational Biomedicine, Heidelberg University, Heidelberg, Germany.

Molecular knowledge of biological processes is a cornerstone in omics data analysis. Applied to single-cell data, such analyses provide mechanistic insights into individual cells and their interactions. However, knowledge of intercellular communication is scarce, scattered across resources, and not linked to intracellular processes. To address this gap, we combined over 100 resources covering interactions and roles of proteins in inter- and intracellular signaling, as well as transcriptional and post-transcriptional regulation. We added protein complex information and annotations on function, localization, and role in diseases for each protein. The resource is available for human, and via homology translation for mouse and rat. The data are accessible via OmniPath's web service (https://omnipathdb.org/), a Cytoscape plug-in, and packages in R/Bioconductor and Python, providing access options for computational and experimental scientists. We created workflows with tutorials to facilitate the analysis of cell-cell interactions and affected downstream intracellular signaling processes. OmniPath provides a single access point to knowledge spanning intra- and intercellular processes for data analysis, as we demonstrate in applications studying SARS-CoV-2 infection and ulcerative colitis.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.15252/msb.20209923DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7983032PMC
March 2021

Asc-1 regulates white versus beige adipocyte fate in a subcutaneous stromal cell population.

Nat Commun 2021 03 11;12(1):1588. Epub 2021 Mar 11.

RG Adipocytes & Metabolism, Institute for Diabetes and Obesity, Helmholtz Center Munich, Neuherberg, Germany.

Adipose tissue expansion, as seen in obesity, is often metabolically detrimental causing insulin resistance and the metabolic syndrome. However, white adipose tissue expansion at early ages is essential to establish a functional metabolism. To understand the differences between adolescent and adult adipose tissue expansion, we studied the cellular composition of the stromal vascular fraction of subcutaneous adipose tissue of two and eight weeks old mice using single cell RNA sequencing. We identified a subset of adolescent preadipocytes expressing the mature white adipocyte marker Asc-1 that showed a low ability to differentiate into beige adipocytes compared to Asc-1 negative cells in vitro. Loss of Asc-1 in subcutaneous preadipocytes resulted in spontaneous differentiation of beige adipocytes in vitro and in vivo. Mechanistically, this was mediated by a function of the amino acid transporter ASC-1 specifically in proliferating preadipocytes involving the intracellular accumulation of the ASC-1 cargo D-serine.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41467-021-21826-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7952576PMC
March 2021

Integrative analysis of cell state changes in lung fibrosis with peripheral protein biomarkers.

EMBO Mol Med 2021 Apr 2;13(4):e12871. Epub 2021 Mar 2.

Institute of Lung Biology and Disease and Comprehensive Pneumology Center with the CPC-M bioArchive, Helmholtz Zentrum München, Member of the German Center for Lung Research (DZL), Munich, Germany.

The correspondence of cell state changes in diseased organs to peripheral protein signatures is currently unknown. Here, we generated and integrated single-cell transcriptomic and proteomic data from multiple large pulmonary fibrosis patient cohorts. Integration of 233,638 single-cell transcriptomes (n = 61) across three independent cohorts enabled us to derive shifts in cell type proportions and a robust core set of genes altered in lung fibrosis for 45 cell types. Mass spectrometry analysis of lung lavage fluid (n = 124) and plasma (n = 141) proteomes identified distinct protein signatures correlated with diagnosis, lung function, and injury status. A novel SSTR2+ pericyte state correlated with disease severity and was reflected in lavage fluid by increased levels of the complement regulatory factor CFHR1. We further discovered CRTAC1 as a biomarker of alveolar type-2 epithelial cell health status in lavage fluid and plasma. Using cross-modal analysis and machine learning, we identified the cellular source of biomarkers and demonstrated that information transfer between modalities correctly predicts disease status, suggesting feasibility of clinical cell state monitoring through longitudinal sampling of body fluid proteomes.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.15252/emmm.202012871DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8033531PMC
April 2021

Deep learning the collisional cross sections of the peptide universe from a million experimental values.

Nat Commun 2021 02 19;12(1):1185. Epub 2021 Feb 19.

Department Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany.

The size and shape of peptide ions in the gas phase are an under-explored dimension for mass spectrometry-based proteomics. To investigate the nature and utility of the peptide collisional cross section (CCS) space, we measure more than a million data points from whole-proteome digests of five organisms with trapped ion mobility spectrometry (TIMS) and parallel accumulation-serial fragmentation (PASEF). The scale and precision (CV < 1%) of our data is sufficient to train a deep recurrent neural network that accurately predicts CCS values solely based on the peptide sequence. Cross section predictions for the synthetic ProteomeTools peptides validate the model within a 1.4% median relative error (R > 0.99). Hydrophobicity, proportion of prolines and position of histidines are main determinants of the cross sections in addition to sequence-specific interactions. CCS values can now be predicted for any peptide and organism, forming a basis for advanced proteomics workflows that make full use of the additional information.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41467-021-21352-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7896072PMC
February 2021

Deep Learning-based Propensity Scores for Confounding Control in Comparative Effectiveness Research: A Large-scale, Real-world Data Study.

Epidemiology 2021 May;32(3):378-388

From the Data Science, Pharmaceutical Research and Early Development Informatics (pREDi), Roche Innovation Center Munich (RICM), Penzberg, Germany.

Background: Due to the non-randomized nature of real-world data, prognostic factors need to be balanced, which is often done by propensity scores (PSs). This study aimed to investigate whether autoencoders, which are unsupervised deep learning architectures, might be leveraged to compute PS.

Methods: We selected patient-level data of 128,368 first-line treated cancer patients from the Flatiron Health EHR-derived de-identified database. We trained an autoencoder architecture to learn a lower-dimensional patient representation, which we used to compute PS. To compare the performance of an autoencoder-based PS with established methods, we performed a simulation study. We assessed the balancing and adjustment performance using standardized mean differences, root mean square errors (RMSE), percent bias, and confidence interval coverage. To illustrate the application of the autoencoder-based PS, we emulated the PRONOUNCE trial by applying the trial's protocol elements within an observational database setting, comparing two chemotherapy regimens.

Results: All methods but the manual variable selection approach led to well-balanced cohorts with average standardized mean differences <0.1. LASSO yielded on average the lowest deviation of resulting estimates (RMSE 0.0205) followed by the autoencoder approach (RMSE 0.0248). Altering the hyperparameter setup in sensitivity analysis, the autoencoder approach led to similar results as LASSO (RMSE 0.0203 and 0.0205, respectively). In the case study, all methods provided a similar conclusion with point estimates clustered around the null (e.g., HRautoencoder 1.01 [95% confidence interval = 0.80, 1.27] vs. HRPRONOUNCE 1.07 [0.83, 1.36]).

Conclusions: Autoencoder-based PS computation was a feasible approach to control for confounding but did not perform better than some established approaches like LASSO.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1097/EDE.0000000000001338DOI Listing
May 2021

CD81 marks immature and dedifferentiated pancreatic β-cells.

Mol Metab 2021 Feb 11;49:101188. Epub 2021 Feb 11.

Institute of Diabetes and Regeneration Research, Helmholtz Zentrum München, 85764, Neuherberg, Germany; German Center for Diabetes Research (DZD), D-85764, Neuherberg, Germany; Technische Universität München, School of Medicine, 81675, München, Germany. Electronic address:

Objective: Islets of Langerhans contain heterogeneous populations of insulin-producing β-cells. Surface markers and respective antibodies for isolation, tracking, and analysis are urgently needed to study β-cell heterogeneity and explore the mechanisms to harness the regenerative potential of immature β-cells.

Methods: We performed single-cell mRNA profiling of early postnatal mouse islets and re-analyzed several single-cell mRNA sequencing datasets from mouse and human pancreas and islets. We used mouse primary islets, iPSC-derived endocrine cells, Min6 insulinoma, and human EndoC-βH1 β-cell lines and performed FAC sorting, Western blotting, and imaging to support and complement the findings from the data analyses.

Results: We found that all endocrine cell types expressed the cluster of differentiation 81 (CD81) during pancreas development, but the expression levels of this protein were gradually reduced in β-cells during postnatal maturation. Single-cell gene expression profiling and high-resolution imaging revealed an immature signature of β-cells expressing high levels of CD81 (CD81) compared to a more mature population expressing no or low levels of this protein (CD81). Analysis of β-cells from different diabetic mouse models and in vitro β-cell stress assays indicated an upregulation of CD81 expression levels in stressed and dedifferentiated β-cells. Similarly, CD81 was upregulated and marked stressed human β-cells in vitro.

Conclusions: We identified CD81 as a novel surface marker that labels immature, stressed, and dedifferentiated β-cells in the adult mouse and human islets. This novel surface marker will allow us to better study β-cell heterogeneity in healthy subjects and diabetes progression.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.molmet.2021.101188DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7932895PMC
February 2021

Single-cell molecular profiling of all three components of the HPA axis reveals adrenal ABCB1 as a regulator of stress adaptation.

Sci Adv 2021 Jan 27;7(5). Epub 2021 Jan 27.

Department of Stress Neurobiology and Neurogenetics, Max Planck Institute of Psychiatry, Munich, Bavaria 80804, Germany.

Chronic activation and dysregulation of the neuroendocrine stress response have severe physiological and psychological consequences, including the development of metabolic and stress-related psychiatric disorders. We provide the first unbiased, cell type-specific, molecular characterization of all three components of the hypothalamic-pituitary-adrenal axis, under baseline and chronic stress conditions. Among others, we identified a previously unreported subpopulation of cells involved in stress adaptation in the adrenal gland. We validated our findings in a mouse stress model, adrenal tissues from patients with Cushing's syndrome, adrenocortical cell lines, and peripheral cortisol and genotyping data from depressed patients. This extensive dataset provides a valuable resource for researchers and clinicians interested in the organism's nervous and endocrine responses to stress and the interplay between these tissues. Our findings raise the possibility that modulating ABCB1 function may be important in the development of treatment strategies for patients suffering from metabolic and stress-related psychiatric disorders.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1126/sciadv.abe4497DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7840126PMC
January 2021

Posterior subcapsular cataracts are a late effect after acute exposure to 0.5 Gy ionizing radiation in mice.

Int J Radiat Biol 2021 1;97(4):529-540. Epub 2021 Mar 1.

Institute of Developmental Genetics, Helmholtz Zentrum München GmbH, German Research Center for Environmental Health, Neuherberg, Germany.

Purpose: The long-term effect of low and moderate doses of ionizing radiation on the lens is still a matter of debate and needs to be evaluated in more detail.

Material And Methods: We conducted a detailed histological analysis of eyes from B6C3F1 mice cohorts after acute gamma irradiation (Co source; 0.063 Gy/min) at young adult age of 10 weeks with doses of 0.063, 0.125, and 0.5 Gy. Sham irradiated (0 Gy) mice were used as controls. To test for genetic susceptibility heterozygous mutant mice were used and compared to wild-type mice of the same strain background. Mice of both sexes were included in all cohorts. Eyes were collected 4 h, 12, 18 and 24 months after irradiation. For a better understanding of the underlying mechanisms, metabolomics analyses were performed in lenses and plasma samples of the same mouse cohorts at 4 and 12 h as well as 12, 18 and 24 months after irradiation. For this purpose, a targeted analysis was chosen.

Results: This analysis revealed histological changes particularly in the posterior part of the lens that rarely can be observed by using Scheimpflug imaging, as we reported previously. We detected a significant increase of posterior subcapsular cataracts (PSCs) 18 and 24 months after irradiation with 0.5 Gy (odds ratio 9.3; 95% confidence interval 2.1-41.3) independent of sex and genotype. Doses below 0.5 Gy (i.e. 0.063 and 0.125 Gy) did not significantly increase the frequency of PSCs at any time point. In lenses, we observed a clear effect of sex and aging but not of irradiation or genotype. While metabolomics analyses of plasma from the same mice showed only a sex effect.

Conclusions: This article demonstrates a significant radiation-induced increase in the incidence of PSCs, which could not be identified using Scheimpflug imaging as the only diagnostic tool.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1080/09553002.2021.1876951DOI Listing
March 2021

Non-canonical Wnt/PCP signalling regulates intestinal stem cell lineage priming towards enteroendocrine and Paneth cell fates.

Nat Cell Biol 2021 01 4;23(1):23-31. Epub 2021 Jan 4.

Institute of Diabetes and Regeneration Research, Helmholtz Diabetes Center, Helmholtz Center Munich, Neuherberg, Germany.

A detailed understanding of intestinal stem cell (ISC) self-renewal and differentiation is required to treat chronic intestinal diseases. However, the different models of ISC lineage hierarchy and segregation are subject to debate. Here, we have discovered non-canonical Wnt/planar cell polarity (PCP)-activated ISCs that are primed towards the enteroendocrine or Paneth cell lineage. Strikingly, integration of time-resolved lineage labelling with single-cell gene expression analysis revealed that both lineages are directly recruited from ISCs via unipotent transition states, challenging the existence of formerly predicted bi- or multipotent secretory progenitors. Transitory cells that mature into Paneth cells are quiescent and express both stem cell and secretory lineage genes, indicating that these cells are the previously described Lgr5 label-retaining cells. Finally, Wnt/PCP-activated Lgr5 ISCs are molecularly indistinguishable from Wnt/β-catenin-activated Lgr5 ISCs, suggesting that lineage priming and cell-cycle exit is triggered at the post-transcriptional level by polarity cues and a switch from canonical to non-canonical Wnt/PCP signalling. Taken together, we redefine the mechanisms underlying ISC lineage hierarchy and identify the Wnt/PCP pathway as a new niche signal preceding lateral inhibition in ISC lineage priming and segregation.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41556-020-00617-2DOI Listing
January 2021

Conditional out-of-distribution generation for unpaired data using transfer VAE.

Bioinformatics 2020 12;36(Suppl_2):i610-i617

Institute of Computational Biology, Helmholtz Center Munich, Neuherberg, Germany.

Motivation: While generative models have shown great success in sampling high-dimensional samples conditional on low-dimensional descriptors (stroke thickness in MNIST, hair color in CelebA, speaker identity in WaveNet), their generation out-of-distribution poses fundamental problems due to the difficulty of learning compact joint distribution across conditions. The canonical example of the conditional variational autoencoder (CVAE), for instance, does not explicitly relate conditions during training and, hence, has no explicit incentive of learning such a compact representation.

Results: We overcome the limitation of the CVAE by matching distributions across conditions using maximum mean discrepancy in the decoder layer that follows the bottleneck. This introduces a strong regularization both for reconstructing samples within the same condition and for transforming samples across conditions, resulting in much improved generalization. As this amount to solving a style-transfer problem, we refer to the model as transfer VAE (trVAE). Benchmarking trVAE on high-dimensional image and single-cell RNA-seq, we demonstrate higher robustness and higher accuracy than existing approaches. We also show qualitatively improved predictions by tackling previously problematic minority classes and multiple conditions in the context of cellular perturbation response to treatment and disease based on high-dimensional single-cell gene expression data. For generic tasks, we improve Pearson correlations of high-dimensional estimated means and variances with their ground truths from 0.89 to 0.97 and 0.75 to 0.87, respectively. We further demonstrate that trVAE learns cell-type-specific responses after perturbation and improves the prediction of most cell-type-specific genes by 65%.

Availability And Implementation: The trVAE implementation is available via github.com/theislab/trvae. The results of this article can be reproduced via github.com/theislab/trvae_reproducibility.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/bioinformatics/btaa800DOI Listing
December 2020

Identification and characterization of distinct brown adipocyte subtypes in C57BL/6J mice.

Life Sci Alliance 2021 01 30;4(1). Epub 2020 Nov 30.

Research Group Adipocytes and Metabolism, Institute for Diabetes and Obesity, Helmholtz Zentrum München, Neuherberg, Germany

Brown adipose tissue (BAT) plays an important role in the regulation of body weight and glucose homeostasis. Although increasing evidence supports white adipose tissue heterogeneity, little is known about heterogeneity within murine BAT. Recently, UCP1 high and low expressing brown adipocytes were identified, but a developmental origin of these subtypes has not been studied. To obtain more insights into brown preadipocyte heterogeneity, we use single-cell RNA sequencing of the BAT stromal vascular fraction of C57/BL6 mice and characterize brown preadipocyte and adipocyte clonal cell lines. Statistical analysis of gene expression profiles from brown preadipocyte and adipocyte clones identify markers distinguishing brown adipocyte subtypes. We confirm the presence of distinct brown adipocyte populations in vivo using the markers EIF5, TCF25, and BIN1. We also demonstrate that loss of enhances UCP1 expression and mitochondrial respiration, suggesting that BIN1 marks dormant brown adipocytes. The existence of multiple brown adipocyte subtypes suggests distinct functional properties of BAT depending on its cellular composition, with potentially distinct functions in thermogenesis and the regulation of whole body energy homeostasis.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.26508/lsa.202000924DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7723269PMC
January 2021

A sparse deep learning approach for automatic segmentation of human vasculature in multispectral optoacoustic tomography.

Photoacoustics 2020 Dec 10;20:100203. Epub 2020 Sep 10.

Institute of Computational Biology, Helmholtz Center Munich, Neuherberg, Germany.

Multispectral Optoacoustic Tomography (MSOT) resolves oxy- (HbO) and deoxy-hemoglobin (Hb) to perform vascular imaging. MSOT suffers from gradual signal attenuation with depth due to light-tissue interactions: an effect that hinders the precise manual segmentation of vessels. Furthermore, vascular assessment requires functional tests, which last several minutes and result in recording thousands of images. Here, we introduce a deep learning approach with a sparse-UNET (S-UNET) for automatic vascular segmentation in MSOT images to avoid the rigorous and time-consuming manual segmentation. We evaluated the S-UNET on a test-set of 33 images, achieving a median DICE score of 0.88. Apart from high segmentation performance, our method based its decision on two wavelengths with physical meaning for the task-at-hand: 850 nm (peak absorption of oxy-hemoglobin) and 810 nm (isosbestic point of oxy-and deoxy-hemoglobin). Thus, our approach achieves precise data-driven vascular segmentation for automated vascular assessment and may boost MSOT further towards its clinical translation.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.pacs.2020.100203DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7644749PMC
December 2020

Inhibition of LTβR signalling activates WNT-induced regeneration in lung.

Nature 2020 12 4;588(7836):151-156. Epub 2020 Nov 4.

German Cancer Research Center (DKFZ), Division of Chronic Inflammation and Cancer, Heidelberg, Germany.

Lymphotoxin β-receptor (LTβR) signalling promotes lymphoid neogenesis and the development of tertiary lymphoid structures, which are associated with severe chronic inflammatory diseases that span several organ systems. How LTβR signalling drives chronic tissue damage particularly in the lung, the mechanism(s) that regulate this process, and whether LTβR blockade might be of therapeutic value have remained unclear. Here we demonstrate increased expression of LTβR ligands in adaptive and innate immune cells, enhanced non-canonical NF-κB signalling, and enriched LTβR target gene expression in lung epithelial cells from patients with smoking-associated chronic obstructive pulmonary disease (COPD) and from mice chronically exposed to cigarette smoke. Therapeutic inhibition of LTβR signalling in young and aged mice disrupted smoking-related inducible bronchus-associated lymphoid tissue, induced regeneration of lung tissue, and reverted airway fibrosis and systemic muscle wasting. Mechanistically, blockade of LTβR signalling dampened epithelial non-canonical activation of NF-κB, reduced TGFβ signalling in airways, and induced regeneration by preventing epithelial cell death and activating WNT/β-catenin signalling in alveolar epithelial progenitor cells. These findings suggest that inhibition of LTβR signalling represents a viable therapeutic option that combines prevention of tertiary lymphoid structures and inhibition of apoptosis with tissue-regenerative strategies.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41586-020-2882-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7718297PMC
December 2020

Predicting single-cell gene expression profiles of imaging flow cytometry data with machine learning.

Nucleic Acids Res 2020 11;48(20):11335-11346

Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg 85764, Germany.

High-content imaging and single-cell genomics are two of the most prominent high-throughput technologies for studying cellular properties and functions at scale. Recent studies have demonstrated that information in large imaging datasets can be used to estimate gene mutations and to predict the cell-cycle state and the cellular decision making directly from cellular morphology. Thus, high-throughput imaging methodologies, such as imaging flow cytometry can potentially aim beyond simple sorting of cell-populations. We introduce IFC-seq, a machine learning methodology for predicting the expression profile of every cell in an imaging flow cytometry experiment. Since it is to-date unfeasible to observe single-cell gene expression and morphology in flow, we integrate uncoupled imaging data with an independent transcriptomics dataset by leveraging common surface markers. We demonstrate that IFC-seq successfully models gene expression of a moderate number of key gene-markers for two independent imaging flow cytometry datasets: (i) human blood mononuclear cells and (ii) mouse myeloid progenitor cells. In the case of mouse myeloid progenitor cells IFC-seq can predict gene expression directly from brightfield images in a label-free manner, using a convolutional neural network. The proposed method promises to add gene expression information to existing and new imaging flow cytometry datasets, at no additional cost.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/nar/gkaa926DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7672460PMC
November 2020

Asthma in farm children is more determined by genetic polymorphisms and in non-farm children by environmental factors.

Pediatr Allergy Immunol 2021 Feb 15;32(2):295-304. Epub 2020 Oct 15.

The German Center for Lung Research (DZL), Germany.

Background: The asthma syndrome is influenced by hereditary and environmental factors. With the example of farm exposure, we study whether genetic and environmental factors interact for asthma.

Methods: Statistical learning approaches based on penalized regression and decision trees were used to predict asthma in the GABRIELA study with 850 cases (9% farm children) and 857 controls (14% farm children). Single-nucleotide polymorphisms (SNPs) were selected from a genome-wide dataset based on a literature search or by statistical selection techniques. Prediction was assessed by receiver operating characteristics (ROC) curves and validated in the PASTURE cohort.

Results: Prediction by family history of asthma and atopy yielded an area under the ROC curve (AUC) of 0.62 [0.57-0.66] in the random forest machine learning approach. By adding information on demographics (sex and age) and 26 environmental exposure variables, the quality of prediction significantly improved (AUC = 0.65 [0.61-0.70]). In farm children, however, environmental variables did not improve prediction quality. Rather SNPs related to IL33 and RAD50 contributed significantly to the prediction of asthma (AUC = 0.70 [0.62-0.78]).

Conclusions: Asthma in farm children is more likely predicted by other factors as compared to non-farm children though in both forms, family history may integrate environmental exposure, genotype and degree of penetrance.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1111/pai.13385DOI Listing
February 2021

identification of apoptotic and extracellular vesicle-bound live cells using image-based deep learning.

J Extracell Vesicles 2020 Jul 16;9(1):1792683. Epub 2020 Jul 16.

Faculty of Medicine, Institute for Immunology, Munich, Germany.

The detection of dead cells remains a major challenge due to technical hurdles. Here, we present a novel method, where injection of fluorescent milk fat globule-EGF factor 8 protein (MFG-E8) combined with imaging flow cytometry and deep learning allows the identification of dead cells based on their surface exposure of phosphatidylserine (PS) and other image parameters. A convolutional autoencoder (CAE) was trained on defined pictures and successfully used to identify apoptotic cells . However, unexpectedly, these analyses also revealed that the great majority of PS cells were not apoptotic, but rather live cells associated with PS extracellular vesicles (EVs). During acute viral infection apoptotic cells increased slightly, while up to 30% of lymphocytes were decorated with PS EVs of antigen-presenting cell (APC) exosomal origin. The combination of recombinant fluorescent MFG-E8 and the CAE-method will greatly facilitate analyses of cell death and EVs .
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1080/20013078.2020.1792683DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7480589PMC
July 2020

Determinants of SARS-CoV-2 receptor gene expression in upper and lower airways.

medRxiv 2020 Sep 2. Epub 2020 Sep 2.

The recent outbreak of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), which causes coronavirus disease 2019 (COVID-19), has led to a worldwide pandemic. One week after initial symptoms develop, a subset of patients progresses to severe disease, with high mortality and limited treatment options. To design novel interventions aimed at preventing spread of the virus and reducing progression to severe disease, detailed knowledge of the cell types and regulating factors driving cellular entry is urgently needed. Here we assess the expression patterns in genes required for COVID-19 entry into cells and replication, and their regulation by genetic, epigenetic and environmental factors, throughout the respiratory tract using samples collected from the upper (nasal) and lower airways (bronchi). Matched samples from the upper and lower airways show a clear increased expression of these genes in the nose compared to the bronchi and parenchyma. Cellular deconvolution indicates a clear association of these genes with the proportion of secretory epithelial cells. Smoking status was found to increase the majority of COVID-19 related genes including ACE2 and TMPRSS2 but only in the lower airways, which was associated with a significant increase in the predicted proportion of goblet cells in bronchial samples of current smokers. Both acute and second hand smoke were found to increase ACE2 expression in the bronchus. Inhaled corticosteroids decrease ACE2 expression in the lower airways. No significant effect of genetics on ACE2 expression was observed, but a strong association of DNA- methylation with ACE2 and TMPRSS2- mRNA expression was identified in the bronchus.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1101/2020.08.31.20169946DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7480059PMC
September 2020

LifeTime and improving European healthcare through cell-based interceptive medicine.

Nature 2020 11 7;587(7834):377-386. Epub 2020 Sep 7.

VIB Technology Watch, Ghent, Belgium.

Here we describe the LifeTime Initiative, which aims to track, understand and target human cells during the onset and progression of complex diseases, and to analyse their response to therapy at single-cell resolution. This mission will be implemented through the development, integration and application of single-cell multi-omics and imaging, artificial intelligence and patient-derived experimental disease models during the progression from health to disease. The analysis of large molecular and clinical datasets will identify molecular mechanisms, create predictive computational models of disease progression, and reveal new drug targets and therapies. The timely detection and interception of disease embedded in an ethical and patient-centred vision will be achieved through interactions across academia, hospitals, patient associations, health data management systems and industry. The application of this strategy to key medical challenges in cancer, neurological and neuropsychiatric disorders, and infectious, chronic inflammatory and cardiovascular diseases at the single-cell level will usher in cell-based interceptive medicine in Europe over the next decade.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41586-020-2715-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7656507PMC
November 2020

Publisher Correction to: Protocol of a population-based prospective COVID-19 cohort study Munich, Germany (KoCo19).

BMC Public Health 2020 09 1;20(1):1335. Epub 2020 Sep 1.

Center for International Health, LMU University Hospital, Munich, Germany.

An amendment to this paper has been published and can be accessed via the original article.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12889-020-09394-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7461750PMC
September 2020

Predicting antigen specificity of single T cells based on TCR CDR3 regions.

Mol Syst Biol 2020 08;16(8):e9416

Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany.

It has recently become possible to simultaneously assay T-cell specificity with respect to large sets of antigens and the T-cell receptor sequence in high-throughput single-cell experiments. Leveraging this new type of data, we propose and benchmark a collection of deep learning architectures to model T-cell specificity in single cells. In agreement with previous results, we found that models that treat antigens as categorical outcome variables outperform those that model the TCR and antigen sequence jointly. Moreover, we show that variability in single-cell immune repertoire screens can be mitigated by modeling cell-specific covariates. Lastly, we demonstrate that the number of bound pMHC complexes can be predicted in a continuous fashion providing a gateway to disentangle cell-to-dextramer binding strength and receptor-to-pMHC affinity. We provide these models in the Python package TcellMatch to allow imputation of antigen specificities in single-cell RNA-seq studies on T cells without the need for MHC staining.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.15252/msb.20199416DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7418512PMC
August 2020

Generalizing RNA velocity to transient cell states through dynamical modeling.

Nat Biotechnol 2020 12 3;38(12):1408-1414. Epub 2020 Aug 3.

Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany.

RNA velocity has opened up new ways of studying cellular differentiation in single-cell RNA-sequencing data. It describes the rate of gene expression change for an individual gene at a given time point based on the ratio of its spliced and unspliced messenger RNA (mRNA). However, errors in velocity estimates arise if the central assumptions of a common splicing rate and the observation of the full splicing dynamics with steady-state mRNA levels are violated. Here we present scVelo, a method that overcomes these limitations by solving the full transcriptional dynamics of splicing kinetics using a likelihood-based dynamical model. This generalizes RNA velocity to systems with transient cell states, which are common in development and in response to perturbations. We apply scVelo to disentangling subpopulation kinetics in neurogenesis and pancreatic endocrinogenesis. We infer gene-specific rates of transcription, splicing and degradation, recover each cell's position in the underlying differentiation processes and detect putative driver genes. scVelo will facilitate the study of lineage decisions and gene regulation.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41587-020-0591-3DOI Listing
December 2020

Targeted pharmacological therapy restores β-cell function for diabetes remission.

Nat Metab 2020 02 20;2(2):192-209. Epub 2020 Feb 20.

Institute of Diabetes and Regeneration Research, Helmholtz Diabetes Center, Helmholtz Center Munich, Neuherberg, Germany.

Dedifferentiation of insulin-secreting β cells in the islets of Langerhans has been proposed to be a major mechanism of β-cell dysfunction. Whether dedifferentiated β cells can be targeted by pharmacological intervention for diabetes remission, and ways in which this could be accomplished, are unknown as yet. Here we report the use of streptozotocin-induced diabetes to study β-cell dedifferentiation in mice. Single-cell RNA sequencing (scRNA-seq) of islets identified markers and pathways associated with β-cell dedifferentiation and dysfunction. Single and combinatorial pharmacology further show that insulin treatment triggers insulin receptor pathway activation in β cells and restores maturation and function for diabetes remission. Additional β-cell selective delivery of oestrogen by Glucagon-like peptide-1 (GLP-1-oestrogen conjugate) decreases daily insulin requirements by 60%, triggers oestrogen-specific activation of the endoplasmic-reticulum-associated protein degradation system, and further increases β-cell survival and regeneration. GLP-1-oestrogen also protects human β cells against cytokine-induced dysfunction. This study not only describes mechanisms of β-cell dedifferentiation and regeneration, but also reveals pharmacological entry points to target dedifferentiated β cells for diabetes remission.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s42255-020-0171-3DOI Listing
February 2020

Alveolar regeneration through a Krt8+ transitional stem cell state that persists in human lung fibrosis.

Nat Commun 2020 07 16;11(1):3559. Epub 2020 Jul 16.

Institute of Lung Biology and Disease and Comprehensive Pneumology Center with the CPC-M bioArchive, Helmholtz Zentrum Muenchen, Member of the German Center for Lung Research (DZL), Munich, Germany.

The cell type specific sequences of transcriptional programs during lung regeneration have remained elusive. Using time-series single cell RNA-seq of the bleomycin lung injury model, we resolved transcriptional dynamics for 28 cell types. Trajectory modeling together with lineage tracing revealed that airway and alveolar stem cells converge on a unique Krt8 + transitional stem cell state during alveolar regeneration. These cells have squamous morphology, feature p53 and NFkB activation and display transcriptional features of cellular senescence. The Krt8+ state appears in several independent models of lung injury and persists in human lung fibrosis, creating a distinct cell-cell communication network with mesenchyme and macrophages during repair. We generated a model of gene regulatory programs leading to Krt8+ transitional cells and their terminal differentiation to alveolar type-1 cells. We propose that in lung fibrosis, perturbed molecular checkpoints on the way to terminal differentiation can cause aberrant persistence of regenerative intermediate stem cell states.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41467-020-17358-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7366678PMC
July 2020

Protocol of a population-based prospective COVID-19 cohort study Munich, Germany (KoCo19).

BMC Public Health 2020 Jun 30;20(1):1036. Epub 2020 Jun 30.

Center for International Health, LMU University Hospital, Munich, Germany.

Background: Due to the SARS-CoV-2 pandemic, public health interventions have been introduced globally in order to prevent the spread of the virus and avoid the overload of health care systems, especially for the most severely affected patients. Scientific studies to date have focused primarily on describing the clinical course of patients, identifying treatment options and developing vaccines. In Germany, as in many other regions, current tests for SARS-CoV2 are not conducted on a representative basis and in a longitudinal design. Furthermore, knowledge about the immune status of the population is lacking. Nonetheless, these data are needed to understand the dynamics of the pandemic and hence to appropriately design and evaluate interventions. For this purpose, we recently started a prospective population-based cohort in Munich, Germany, with the aim to develop a better understanding of the state and dynamics of the pandemic.

Methods: In 100 out of 755 randomly selected constituencies, 3000 Munich households are identified via random route and offered enrollment into the study. All household members are asked to complete a baseline questionnaire and subjects ≥14 years of age are asked to provide a venous blood sample of ≤3 ml for the determination of SARS-CoV-2 IgG/IgA status. The residual plasma and the blood pellet are preserved for later genetic and molecular biological investigations. For twelve months, each household member is asked to keep a diary of daily symptoms, whereabouts and contacts via WebApp. If symptoms suggestive for COVID-19 are reported, family members, including children < 14 years, are offered a pharyngeal swab taken at the Division of Infectious Diseases and Tropical Medicine, LMU University Hospital Munich, for molecular testing for SARS-CoV-2. In case of severe symptoms, participants will be transferred to a Munich hospital. For one year, the study teams re-visits the households for blood sampling every six weeks.

Discussion: With the planned study we will establish a reliable epidemiological tool to improve the understanding of the spread of SARS-CoV-2 and to better assess the effectiveness of public health measures as well as their socio-economic effects. This will support policy makers in managing the epidemic based on scientific evidence.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12889-020-09164-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7324773PMC
June 2020

The proteome landscape of the kingdoms of life.

Nature 2020 06 17;582(7813):592-596. Epub 2020 Jun 17.

Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany.

Proteins carry out the vast majority of functions in all biological domains, but for technological reasons their large-scale investigation has lagged behind the study of genomes. Since the first essentially complete eukaryotic proteome was reported, advances in mass-spectrometry-based proteomics have enabled increasingly comprehensive identification and quantification of the human proteome. However, there have been few comparisons across species, in stark contrast with genomics initiatives. Here we use an advanced proteomics workflow-in which the peptide separation step is performed by a microstructured and extremely reproducible chromatographic system-for the in-depth study of 100 taxonomically diverse organisms. With two million peptide and 340,000 stringent protein identifications obtained in a standardized manner, we double the number of proteins with solid experimental evidence known to the scientific community. The data also provide a large-scale case study for sequence-based machine learning, as we demonstrate by experimentally confirming the predicted properties of peptides from Bacteroides uniformis. Our results offer a comparative view of the functional organization of organisms across the entire evolutionary range. A remarkably high fraction of the total proteome mass in all kingdoms is dedicated to protein homeostasis and folding, highlighting the biological challenge of maintaining protein structure in all branches of life. Likewise, a universally high fraction is involved in supplying energy resources, although these pathways range from photosynthesis through iron sulfur metabolism to carbohydrate metabolism. Generally, however, proteins and proteomes are remarkably diverse between organisms, and they can readily be explored and functionally compared at www.proteomesoflife.org.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41586-020-2402-xDOI Listing
June 2020